Artificial intelligence is increasingly being explored in microfluidics, from device design optimization and image analysis to automated experimental control. Yet one challenge remains particularly practical: how can researchers translate a written experimental protocol into a sequence of instructions that microfluidic instruments can execute?
AI-assisted microfluidic automation offers a new approach to this challenge.
Instead of manually programming every pump movement, valve position and timing condition, researchers can use natural language to describe their fluid handling process. An AI assistant can then help generate a structured workflow based on the available instruments and a predefined sequence format.
Combined with dedicated microfluidic control software, this approach can simplify the development of automated experiments involving syringe pumps, rotary valves and other fluidic components.
The objective is not to replace engineering expertise or allow artificial intelligence to control experimental hardware autonomously. It is to make the transition from an experimental protocol to an executable workflow more accessible, while maintaining control over instrument settings, physical constraints and sequence validation.
Microfluidic technology enables precise manipulation of small fluid volumes, making it particularly useful in applications such as sample preparation, biosensing, chemical synthesis and analytical instrumentation.
However, operating a microfluidic device is only one part of an automated experiment.
A typical microfluidic system may combine several components, each performing a specific function:
Consider an automated sample preparation process involving several reagents.
The experimental protocol might require selecting a reagent, aspirating a precise volume, transferring it into a reaction chamber, waiting for a chemical reaction and repeating the operation with another liquid.
While these instructions are understandable to a laboratory operator, an automated system requires more specific information.
Each step must identify the appropriate instrument, the required operation and its parameters. The control software must also coordinate the order of execution and account for the configuration of the connected devices.
For example, switching a valve before a syringe pump has completed its movement may produce a different fluidic result than intended.
Similarly, an incorrect syringe volume, flow rate or valve position can compromise the experiment.
This translation from experimental intent to instrument commands is often a time-consuming stage of microfluidic system development.
AI in microfluidics covers several technological approaches.
Machine learning and deep learning models are being investigated for applications such as droplet detection, image analysis, prediction of fluidic behavior and optimization of experimental conditions.
These methods typically rely on experimental data, trained models or feedback from sensors and imaging systems.
Generative AI provides a different possibility: translating natural language into structured instrument instructions.
Large language models can interpret descriptions of experimental procedures and produce structured outputs following supplied examples.
Rather than predicting droplet size or optimizing a microfluidic chip design, the objective is to prepare the sequence of actions required to operate an automated fluid handling system.
For example, a researcher might write:
“Select reagent A, aspirate 100 µL at 200 µL/min, dispense the liquid into the reaction chamber at 100 µL/min, wait five seconds and repeat the procedure three times.”
An AI assistant can break this description into individual actions:
However, converting these instructions into an executable workflow requires more than understanding the language.
The AI must also follow the specific command structure supported by the control software.
A useful approach is to provide an AI assistant with an example of a valid instrument sequence.
This reference establishes the expected file structure, the syntax of supported operations and the parameters required by the software.
The user can then describe the experimental process and ask the AI to generate a new sequence following the same structure.
This approach can reduce repetitive sequence-building work, particularly during early-stage development when experimental conditions change frequently.
It also has limitations.
A language model may misunderstand instructions, introduce unsupported commands or propose parameters that are incompatible with the physical system.
For this reason, the generated output must be considered a first draft requiring verification, not an automatically validated experimental procedure.
Consider a fluidic system designed to automate reagent selection and transfer.
The setup uses an AMF syringe pump and a rotary valve to perform a controlled liquid handling sequence.
For this example, the fluidic circuit connects the syringe pump to a reagent reservoir and a reaction chamber through the valve.
The objective is to transfer a defined reagent volume, apply a waiting period and repeat the operation.
Before generating any instructions, the operator must identify the parameters of the experiment.
These parameters define the intended experimental process.
They do not, by themselves, guarantee that the fluidic configuration is suitable. The operator must also consider the available syringe capacity, liquid routing, dead volumes and the physical connections between the instruments.
The experimental requirements can then be translated into a natural language prompt.
Example prompt:
Create an AMF Control Center sequence for an automated reagent handling experiment. The system contains one AMF syringe pump and one RVM rotary valve. Follow the structure and supported commands from the attached AMF Control Center example sequence. The experiment should: Select reagent A connected to valve port 1. Aspirate 100 µL at 200 µL/min. Switch to valve port 2, connected to the reaction chamber. Dispense 100 µL at 100 µL/min. Wait five seconds. Repeat this procedure three times. Generate a structured workflow that can be reviewed in AMF Control Center. Do not introduce unsupported commands or invent missing instrument parameters.
Create an AMF Control Center sequence for an automated reagent handling experiment.
The system contains one AMF syringe pump and one RVM rotary valve.
Follow the structure and supported commands from the attached AMF Control Center example sequence.
The experiment should:
Generate a structured workflow that can be reviewed in AMF Control Center. Do not introduce unsupported commands or invent missing instrument parameters.
The AI uses both the written instructions and the supplied sequence example to prepare a corresponding file.
The reference sequence is essential because it guides the model towards the structure expected by the software.
The resulting sequence should represent the fluidic operations in the correct order, with each instruction assigned to the appropriate instrument.
A simplified representation could look like this:
Illustrative workflow logic, not the native JSON format of AMF Control Center.
At this stage, the AI has helped translate the experimental protocol into a structured sequence.
The next challenge is determining whether the proposed instructions can be safely and correctly executed by the actual microfluidic system.
A generated sequence should never be executed without verification.
This is especially important in microfluidic applications where accurate liquid handling depends on both software instructions and physical operating conditions.
For example, a sequence may contain valid instrument commands but still be inappropriate for a particular experiment due to incorrect fluidic routing, insufficient syringe capacity or incompatible operating conditions.
AMF Control Center provides an environment for reviewing and executing fluidic workflows using compatible AMF instruments.
After generating a workflow file with an external AI assistant, the operator can open it in AMF Control Center.
The Sequence view displays the different instrument actions, allowing users to inspect the proposed operations and adjust the relevant parameters.
This makes it possible to identify differences between the experimental protocol and the AI-generated instructions before running the system.
AMF Control Center includes a Build function designed to check programmed sequences before execution.
Build simulates the sequence logic and identifies certain issues, such as:
These checks provide an additional layer of verification when working with AI-generated instructions.
However, they do not constitute a complete physical simulation of the microfluidic circuit.
Build does not replace verification of fluidic connections, liquid compatibility, pressure conditions, contamination risks or experimental performance.
Software validation checks the instructions. Experimental validation confirms that the physical system performs as intended.
Once the sequence has been reviewed and the necessary checks completed, it can be executed using the connected instruments.
Microfluidic automation becomes more complex as the number of instruments increases.
An automated process may involve several syringe pumps operating at different flow rates, multiple rotary valves selecting different reagents and sequences containing repeated or timed operations.
Rather than treating each instrument independently, a coordinated workflow defines their operations within the same experimental procedure.
AMF Control Center supports several families of AMF components:
LSPone series: laboratory syringe pumps
LSPone combines precise syringe pump operation with integrated valve control, making it suitable for laboratory fluid handling, automated reagent transfer and experimental prototyping.
SPM series: OEM syringe pumps
The SPM series provides controlled liquid handling for instrument integration, including programmable syringe movements, dispensing volumes and flow rates.
RVM series: microfluidic rotary valves
RVM valves provide programmable fluid selection, switching and distribution between different ports and fluidic paths.
RVM mini: compact rotary valves
The RVM mini extends programmable fluid routing to compact instrument architectures where installation space is limited.
Using AMF Control Center Premium, up to six compatible AMF instruments can be connected and coordinated within fluidic sequences.
A workflow can combine valve movements, syringe pump operations, waits and loops, allowing users to test multi-instrument processes without developing a dedicated control interface from scratch.
AI-assisted generation can help prepare such sequences, especially when multiple instruments and repeated operations are involved.
Nevertheless, the engineer must verify the overall fluidic architecture and the interactions between the connected components.
The main potential benefit of generative AI in microfluidic workflow development lies in reducing the effort required to translate experimental procedures into structured software instructions.
This can be particularly relevant in three situations.
During early development, experimental protocols frequently evolve.
Researchers may adjust volumes, flow rates, reagent selection, incubation times or sequence repetitions to investigate different conditions.
AI-assisted sequence generation can help prepare revised workflow drafts from updated natural language instructions.
This may reduce repetitive programming work, although each modified sequence still requires appropriate validation.
Coordinating several microfluidic components introduces additional complexity.
A workflow must assign the correct operations to the correct instruments and maintain the intended order of execution.
Providing an AI assistant with a structured sequence reference can help organize these operations into an initial workflow.
For complex systems, this does not eliminate the need for engineering review, but it can provide a useful starting point.
Not every researcher developing a fluidic experiment is a software developer.
Graphical control software already reduces the need to write dedicated instrument commands. Generative AI can complement this approach by helping translate a familiar experimental description into a structured sequence.
The user remains responsible for reviewing the result, but the initial interaction can begin with the experimental procedure rather than with programming syntax.
The potential benefit is therefore not only faster sequence preparation, but also a more accessible starting point for researchers working with automated liquid handling systems.
AMF Control Center brings these different steps together through a practical workflow.
The software itself does not contain an integrated AI model. Instead, users can work with an external AI assistant to prepare a structured sequence file using an example workflow as a reference.
AMF Control Center deliberately separates AI-assisted sequence generation from instrument control. This architectural choice offers several advantages:
Offline operation and IT security: AMF Control Center does not require an internet connection to operate. This reduces network dependencies and can simplify deployment in laboratories with strict IT security policies.
Simplified IT qualification: Without an embedded cloud-based AI service, the software avoids additional external connections and data exchanges, potentially making internal IT reviews and software qualification easier.
No AI subscription or licensing dependencies: Users can choose their preferred external AI assistant to prepare sequences, without being tied to a specific AI provider or an additional AI subscription managed through AMF Control Center.
Greater control over experimental data: Users decide whether, when and what information they share with an external AI service. Generated sequences can then be reviewed and executed locally.
The principle is simple: AI assists with workflow preparation, while AMF Control Center handles instrument control independently.
The process follows five main stages:
Describe: Define the experiment in natural language, specifying the instruments, fluidic operations and required parameters.
Generate: Use an external AI assistant to draft a structured sequence based on an existing AMF Control Center example.
Import: Open the generated sequence in AMF Control Center to inspect the proposed instrument commands.
Validate: Review the sequence, adjust the parameters and use Build to identify supported command and sequence errors.
Execute: Run the validated workflow with the connected AMF instruments after confirming the physical configuration.
This method keeps a clear separation between AI-assisted sequence preparation and instrument-level execution.
It also allows users to build and modify workflows manually through the graphical interface without using AI.
For developers who need additional flexibility, sequences can be exported to Python using the AMF Python SDK. This provides a starting point for incorporating fluidic operations into larger software applications.
The result is an approach that connects experimental descriptions, structured workflows and physical microfluidic instrumentation, while keeping the operator responsible for the final sequence.
Artificial intelligence is opening different possibilities for microfluidic technology, from predictive models and image analysis to the optimization of complex experimental systems.
Generative AI introduces another opportunity: helping researchers translate natural language protocols into structured sequences for automated instruments.
Combined with dedicated control software, this approach can simplify the preparation of microfluidic workflows, support experimental prototyping and facilitate the coordination of multiple fluid handling components.
However, the distinction between generating instructions and validating an experiment remains essential.
A correctly formatted sequence is not necessarily a correct fluidic process.
Experimental performance still depends on instrument capabilities, fluidic architecture, physical conditions and engineering decisions.
AI does not replace fluidic engineering. It helps translate engineering decisions into executable instructions.
By combining AI-assisted sequence generation with graphical programming, instrument control and sequence validation, AMF Control Center offers a practical way to explore this approach.
From individual syringe pump control to multi-instrument workflows, AMF Control Center provides a common interface for developing and executing automated fluidic sequences.
Discover how it can support your experimental development, whether you are preparing a laboratory protocol or prototyping an integrated microfluidic system.
AI-assisted microfluidic automation refers to the use of artificial intelligence to support tasks involved in automating microfluidic experiments. Depending on the application, AI may be used for design optimization, image analysis, experimental data processing or the generation of instrument control instructions.
In workflow development, generative AI can help translate a written experimental protocol into a structured sequence of operations for pumps and valves.
AI-generated instructions can be used to prepare workflows for microfluidic control software, but direct instrument control requires a compatible hardware and software interface.
In the AMF Control Center approach, an external AI assistant generates a sequence draft. The user then imports, reviews and checks the sequence before executing it through the software.
AI can assist in preparing the instructions needed to coordinate syringe pumps and rotary valves, such as aspirating a volume, selecting a valve port, dispensing at a specified flow rate or repeating an operation.
Actual execution depends on the supported instruments, software commands and physical configuration of the fluidic system.
No. Generative AI can produce structured sequence drafts from natural language instructions and reference examples without requiring users to train a dedicated machine learning model.
This differs from predictive applications that rely on experimental datasets, neural networks or trained models to optimize fluidic behavior.
No. AMF Control Center provides a graphical interface for controlling compatible instruments and building sequences. Python export is available for users who want to extend their workflows into more advanced software applications.
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